Pedestrian counting and detection at a traffic intersection based on object movement within a field of view

ABSTRACT

Pedestrian detection and counting for traffic intersection control analyzes characteristics of a field of view of a traffic detection zone to determine a location and size of a pedestrian area, and applies protocols for evaluating pixel content in the field of view to identify individual pedestrians. The location and size of a pedestrian area is determined based either on locations of vehicle and bicycle detection areas or on movement of various objects within the field of view. Automatic pedestrian speed calibration with a region of interest for pedestrian detection is accomplished using lane and other intersection markings in the field of view. Detection and counting further includes identifying a presence, volume, velocity and trajectory of pedestrians in the pedestrian area of the traffic detection zone.

FIELD OF THE INVENTION

The present invention relates to the field of traffic detection.Specifically, the present invention relates to determining a region in afield of view of the traffic detection zone used by one or morepedestrians, and identifying and counting pedestrians in a trafficdetection zone for intersection traffic control.

BACKGROUND OF THE INVENTION

There are many conventional traffic detection systems for intersectioncontrol. Conventional systems typically utilize sensors, either in theroadway itself, or positioned at a roadside location or on trafficlights proximate to the roadway. Common types of vehicular sensors areinductive coils, or loops, embedded in a road surface, and videocameras, radar sensors, acoustic sensors, and magnetometers, either inthe road itself, or at either the side of a roadway or positioned higherabove traffic to observe and detect vehicles in a desired area. Eachtype of sensor provides information used to determine a presence ofvehicles in specific traffic lanes, to provide information for properactuation of traffic signals.

These conventional detection systems are commonly set up with ‘virtualzones’, which are hand- or machine-drawn areas on an image where objectsmay be moving or present. Traditionally, a vehicle passes through orstops in a zone, and these zones generate an “output” when an object isdetected as passing through or resting within all or part of the zone.Many detection systems are capable of detecting different types ofvehicles, such as cars, trucks, bicycles, motorcycles, pedestrians, etc.This is accomplished by creating special zones within a field of view todifferentiate objects, such as bicycle zones and pedestrian zones.Therefore, conventional detection systems are capable ofdifferentiating, for example, bicycles from other types of vehicles byanalyzing these special zones.

Outputs are sent to external devices or locations for use or storage,such as for example to a traffic signal controller, which performscontrol and timing functions based on the information provided. Theseoutputs also provide traffic planners and engineers with information onthe volume of traffic at key points in a traffic network. Thisinformation is important for comparing volumes over periods of time tohelp with accurate adjustment of signal timing and managing trafficflow. Current systems and methods of traffic detection provide data thatresults only from a count of a total number of vehicles, which may ormay not include bicycles or other road users, as therefore there is noway differentiating between different types of vehicles. As the need formodified signal timing to accommodate bicyclists, pedestrians and othersbecomes more critical for proper traffic management, a method forseparating the count of all modes of use on a thoroughfare is needed toimprove the ability to accurately manage traffic environments.

Traffic planners and engineers require data on the volume of pedestriantraffic at key points in a traffic network. This data is important forcomparing volumes over periods of time to help with accurate adjustmentof signal timing. No current method for automatic count and datacollection for pedestrian activity exists in a traffic detection system.As the need for modified signal timing to accommodate roadway users suchas pedestrians becomes more critical for proper traffic management, amethod for accurately identifying and counting pedestrians using aroadway intersection would greatly improve the ability to efficientlymanage traffic environments.

BRIEF SUMMARY OF THE INVENTION

It is therefore one objective of the present invention to provide asystem and method of determining a pedestrian area within a trafficdetection zone for traffic intersection control. It is another objectiveof the present invention to provide a system and method of determining apedestrian area within a traffic detection zone based on the location ofone or both of a vehicle detection zone(s) and a bicycle detectionzone(s). It is still another objective to provide a system and method ofdetermining a pedestrian area within a traffic detection zone based onmovement of various objects in a field of view of the traffic detectionzone, such as pedestrians, vehicles, and bicycles.

It is a further objective of the present invention to provide a systemand method of accurately counting pedestrians within a traffic detectionzone for traffic intersection control. It is yet another objective ofthe present invention to provide a system and method of identifyingcharacteristics of a pedestrian to improve count accuracy. Anotherobjective of the present invention is to incorporate part-based objectrecognition to identify characteristics of a pedestrian within a fieldof a view of a traffic detection zone.

Yet another objective of the present invention is to automaticallycalibrate a traffic detection system by calculating pedestrian speed ina field of view for improved traffic intersection control. A furtherobjective is to provide a system and method of identifying pedestrianincidents in a traffic detection zone, and triggering an alarm based onpedestrian incidents. It is still a further objective of the presentinvention to combine vehicle detection, bicycle detection, andpedestrian detection in a whole scene analysis of a field of view fortraffic intersection control.

The present invention provides systems and methods of identifying apresence, volume, velocity and trajectory of pedestrians in a region ofinterest in a field of view of a traffic detection zone. These systemsand methods present an approach to traffic intersection control thatincludes, in aspect embodiment, both identification of a pedestriandetection zone in the field of view, and identification of individualpedestrians in the pedestrian detection zone. This approach, styled as apedestrian zone detection, identification and counting framework, enableimproved pedestrian counting in the pedestrian detection zone, andincreased accuracy in various aspects of roadway management.

Identification of a pedestrian detection zone in the field of view inthe present invention is performed, in one embodiment, using a zoneposition analysis that automatically determines a pedestrian area in anintersection based on locations of one or both of vehicle and bicycledetection zones. Such vehicular and bicycle detection zones are eitherthemselves automatically determined in a field of view, or drawn by auser. Regardless, knowledge of the location of these zones allows thepresent invention to calculate a pedestrian detection zone based ontheir position relative to a stop bar at a traffic intersection.

Identification of a pedestrian detection zone in the field of view inthe present invention is performed, in another embodiment, using anobject movement analysis automatically determines a pedestrian area inan intersection based on movement of various other objects within thefield of view irrespective of the location of other detection zones.These objects include vehicles, motorcycles, bicycles, pedestrians, andany other moving objects that may be detected by sensors capturing datain the field of view. Regardless, analysis of image pixel activity ofthese moving objects allows the present invention to calculate apedestrian detection zone.

Identification of individual pedestrians in the pedestrian detectionzone in the present is performed, in one embodiment, by comparing apart-based object recognition analysis with a model of a single walkingpedestrian to differentiate individual pedestrians from groups of movingpedestrians. Such a comparison analyzes image characteristics toseparate groups of pedestrians for improved count accuracy.

The present invention also includes calibration of pedestrian speed intraffic intersection control. In this embodiment, the pedestrian zonedetection, identification and counting framework locates a region ofinterest based on locations of intersection or pavement markings andlane structures, such as a stop bar and lane lines, and computesfeatures of an image inside the region of interest to calculate thepedestrian speed.

The present invention also includes incident detection in trafficintersection control. In this embodiment, the pedestrian zone detection,identification and counting framework learns a background of thepedestrian detection zone, and looks for changes in the background toidentify non-moving objects such as prone objects or pedestrians orunauthorized vehicles. Identification of such non-moving objectsinitiate an alarm for responsible authorities to improve emergencyresponse and efficient intersection performance.

Other objects, embodiments, features and advantages of the presentinvention will become apparent from the following description of theembodiments, taken together with the accompanying drawings, whichillustrate, by way of example, the principles of the invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate several embodiments of theinvention and together with the description, serve to explain theprinciples of the invention.

FIG. 1 is a system diagram for a pedestrian zone detection,identification and counting according to one aspect of the presentinvention;

FIG. 2 is a flowchart of steps performed for pedestrian zone detection,identification and counting according to one aspect of the presentinvention;

FIG. 3 is a flowchart of steps performed for calibrating a pedestrianspeed in pedestrian zone detection, identification and countingaccording to another aspect of the present invention;

FIG. 4 is a flowchart of steps performed for incident detection in thepedestrian zone detection and pedestrian identification and countingaccording to one embodiment of the present invention;

FIG. 5 is an exemplary representation of a field of view in a trafficdetection zone, showing in a particular a region of interest forpedestrian detection according to the present invention;

FIG. 6 is another exemplary representation of a field of view in atraffic detection zone, showing vehicular, bicycle, and pedestriandetection zones according to one embodiment of the present invention;and

FIG. 7 is further exemplary representation of a field of view in atraffic detection zone, showing accumulated tracks of vehicles andpedestrians according to one embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

In the following description of the present invention reference is madeto the exemplary embodiments illustrating the principles of the presentinvention and how it is practiced. Other embodiments will be utilized topractice the present invention and structural and functional changeswill be made thereto without departing from the scope of the presentinvention.

FIG. 1 is a system diagram illustrating elements of a pedestriantracking and counting framework 100, according to one aspect of thepresent invention. The pedestrian tracking and counting framework 100 isperformed within one or more systems and/or methods that includesseveral components, each of which define distinct activities fordefining an area used by pedestrians 102 in a traffic detection zone114, and accurately counting pedestrians 102 in such an area, fortraffic intersection control.

FIG. 5-7 are exemplary screenshot images 111 of a field of view 112 in atraffic detection 114. In the exemplary image 111 of FIG. 5, a region ofinterest 103 is highlighted for a pedestrian detection zone 104, and apedestrians 102 are shown present therein. In the exemplary image 111 ofFIG. 6, the pedestrian detection zone 104 is shown below user-drawnvehicular and bicycle detection zones 105 in the field of view 112. Inthe exemplary image of FIG. 7, arrows indicate accumulated tracks 106 ofmoving objects 107 are shown therein, as are pedestrian tracks 108 inthe region of interest 103. Each of FIG. 5-7 also show standardintersection roadway markings and lane structures 109.

Returning to FIG. 1, the pedestrian tracking and counting framework 100ingests, receives, requests, or otherwise obtains input data 110 thatrepresents a field of view 112 of the traffic detection zone 114. Inputdata 110 is collected from the one or more sensors 120, which may bepositioned in or near a roadway area for which the traffic detectionzone 114 is identified and drawn. The one or more sensors 120 includevideo systems 121 such as cameras, thermal cameras, radar systems 122,magnetometers 123, acoustic sensors 124, and any other devices orsystems 125 which are capable of detecting a presence of objects withina traffic intersection environment.

The input data 110 is applied to a plurality of data processingcomponent 140 within a computing environment 130 that also includes oneor more processors 132, a plurality of software and hardware components,and one or more modular software and hardware packages configured toperform specific processing functions. The one or more processors 132,plurality of software and hardware components, and one or more modularsoftware and hardware packages are configured to execute programinstructions to perform algorithms for various functions within thepedestrian tracking and counting framework 100 that are described indetail herein, and embodied in the one or more data processing modules140.

The plurality of data processing components 140 include a data ingestcomponent 141 configured to ingest, receive, request, or otherwiseobtain input data 110 as noted above, and a pedestrian zone detectionand counting initialization component 142 configure to initialize thepedestrian tracking and counting framework 100 and retrieval of inputdata 110 for performing the various functions of the present invention.The plurality of data processing modules 140 also include a pedestrianzone identification component 143, an image processing and pedestriandetection learning component 144, a speed calibration component 145, anincident detection component 146, and a counting component 147.

At least some of these data processing components 140 are configured togenerate output data 180 that may take many different forms. Output data180 may include of a pedestrian count, generated by the countingcomponent 147 according to one or more embodiments of the presentinvention. Output data 180 may also include a calibrated pedestrianspeed, generated by the speed calibration component 145 according toanother embodiment of the present invention. Output data 180 may furtherinclude an alarm indicated an incident detected in a pedestrian area104, generated by the incident detection component 146 according tostill another embodiment of the present invention. Output data 180 mayalso be provided for additional analytics and processing in one or morethird party or external applications 190. These may include a trafficmanagement tool 191, a zone and lane analysis component 192, a trafficmanagement system 193, and a signal controller 194.

The pedestrian zone identification component 143 is configured to definea pedestrian detection zone 104 in the field of view 112 of the trafficdetection zone 114 for subsequent counting of pedestrians 102 therein.Differential analytical approaches 160 may be applied to achieve thisdetermination. In one embodiment, the pedestrian zone identificationcomponent 143 applies a zone position analysis 161 that determines thepedestrian detection zone 104 based on locations of one or more ofvehicle and bicycle detection zones 105 in the field of view 112.

Vehicle and bicycle detection zones 105 are typically drawn in variousplaces in the field of view 112 depending on user requirements. In mostsituations, the user requires detection at or near the stop bar.Detection zones 105 are usually drawn above the stop bar, and analgorithm is applied to identify the detection zones 105 nearest to thestop bar. An area comprised of a pedestrian strip is created up to thetop line of these zones 105, extending from the left to right edge ofthe field of view 112 below the top lines of the zones 105. Thepedestrian strip height is determined by a calculation of the vehicleand bicycle zone heights, and may be extended to cover a larger areathat is more likely to be used by all pedestrians 102.

The zone position analysis 161 therefore accomplishes defining apedestrian detection zone 104 by identifying a position of at least onevehicle detection zone 105 and at least one bicycle detection zone 105in nearest proximity to a stop bar, with each of the at least onevehicle detection zone 105 and the at least one bicycle detection zone105 have a height that extends to or near to the stop bar. Next, thezone position analysis 161 calculates a height of a pedestrian strip inthe field of view 112 from the height of the at least one vehicledetection zone 105 and the height of the at least one bicycle detectionzone 105, and extends a length of the pedestrian strip to a leftmostedge of the field of view 112, and a rightmost edge of the field of view112. As noted above, the zone position analysis 161 may also extend theheight of the pedestrian strip into a portion of the at least onevehicle detection zone 105 and into a portion of the at least onebicycle detection zone 105.

In another embodiment, the pedestrian zone identification component 143applies an object movement analysis 162 that determines the pedestriandetection zone 104 based on movement of various objects within the fieldof view 112, such as vehicles, bicycles, and other pedestrians 102. Thisanalysis 162 does not rely upon any other data, such as the locations ofvehicle and bicycle detection zones 105 in the field of view 112, oruser drawing of such zones 105.

The object movement analysis 162 determines the area of the field ofview 112 where pedestrians 102 typically enter the roadway, byidentifying and differentiating pedestrians 102 from other roadway usersand tracking their position as the move through the field of view 112.Pedestrians 102 have characteristics that differ markedly from otherroadway objects, such as vehicles and bicycles. These characteristicsinclude, but are not limited to, size, gesture, speed, and entry andpoints in the field of view 112. Standard intersection roadway markingsand lane structures 109 may also be used to identify areas wherepedestrians 102 should be traveling.

Once the pedestrian zone identification component 143 identifies normalpedestrian tracks 108 in the field of view 112, a boundary box iscreated and the area can then be used to collect additional data fromvarious analytics, such as determining count, speed, trajectory, andgrouping of pedestrians 102. Additionally, by analyzing the motionstrength and frequency of activity of each pixel, the pedestrian zoneidentification component 143 obtains accumulated tracks 106 of movingobjects 107 in the field of view 112. This enables refining the boundaryof pedestrian detection zone 104, as well as other detection zones 105.

The object movement analysis 162 therefore accomplishes defining apedestrian detection zone 104 by ascertaining a region of interest 103in the field of view 112 for pedestrian tracks 108, based on at leastone of lane structures and intersection road markings 109 and movementof pixels representing moving objects 107 relative to those lanestructures and intersection road markings 109. Accumulated tracks 106 ofmoving objects 107 are determined in the field of view 112 by analyzingmotion strength and frequency of activity of each pixel representing themoving objects 107 in the field of view 112. The present invention alsotracks pedestrian characteristics in the region of interest 103 todistinguish the accumulated tracks 106 of the moving objects 107 fromthe pedestrian tracks 108. Analyzing motion strength of pixels in theobject movement analysis 162 may include computing a binary thresholdedimage defining a histogram of oriented gradient features that furtherdefine a pedestrian contour, and updating the histogram as pixelactivity occurs in the changing image. Analyzing a frequency of pixelactivity may include computing an activity frequency threshold andfinding accumulated tracks 106 from pixel frequency activity.

The image processing and pedestrian detection learning component 144 isconfigured to detect one or more pedestrians 102 in the pedestrian zone104 from similarities of a single walking pedestrian model withpart-based object recognition of individual pedestrians 102, andincrement a count for the counting module 147. Multiple analyticalapproaches 170 may therefore be applied to detect the one or morepedestrians 102 for the counting module 147. In one embodiment, theimage processing and pedestrian detection learning component 144 appliesa part-based object recognition analysis 171 and image analysis using ahistogram of oriented gradient features 172 to develop a model 173 ofthe single walking pedestrian.

The image processing and pedestrian detection learning component 144applies these analytical approaches 170 by, in one aspect of the presentinvention, analyzing portions of the field of view 112 by moving asliding window through the pedestrian detection zone 104 in the field ofview 112, and computing features of current pixel content identified inthe sliding window by identifying part-based features that define anindividual pedestrian 102. The part-based features include one or moreof body structure combinations, body shape, body width and walkinggestures. In this part-based object recognition analysis 171, the imageprocessing and pedestrian detection learning component 144 alsodetermines a width and a height of one or more object parts, comparesbody structure combinations with one or more predetermined templates,and applies one or more geometric constraints to separate the part-basedfeatures.

The image processing and pedestrian detection learning component 144then proceeds with developing the model 173 of a single walkingpedestrian to separate each individual pedestrian in a group of movingpedestrians in the field of view 112. This is accomplished by computinga histogram of oriented gradient pedestrian features 172 based on pixelsdefining a pedestrian contour. The image processing and pedestriandetection learning component 144 next determines a matching confidencebetween an individual pedestrian and a group of moving pedestrians bycalculating a mathematical similarity between the computed features ofcurrent pixel content and the model of the single walking pedestrian173. Where a matching confidence is high, this indicates that anindividual pedestrian has been identified, and the present inventionincrements a pedestrian count in the counting component 147. Where amatching confidence is low, the present invention analyzes the nextportion of the field of view 112 by moving the sliding window to thenext position in the field of view 112 for further image processing.

FIG. 2 is a flowchart illustrating steps in a process 200 for performingthe pedestrian tracking and counting framework 100, according to certainembodiments of the present invention. Such a process 200 may include oneor more algorithms for pedestrian zone identification within thecomponent 143, and for image processing and pedestrian detectionlearning within the component 144, and for the various analyticalapproaches applied within each such component.

Pedestrian zone identification and counting in the process 200 areinitialized at step 210 by retrieving input data 110 representing afield of view 112 for a traffic detection zone 114. The process 200 thendetects and defines the pedestrian zone 104, using one of the analyticalapproaches 160, in either step 220 or 230.

At step 220, the process 200 determines and defines a pedestrian zone104 using existing positions of one or more of vehicle and bicycle lanes105 in the traffic detection zone 114. Those, as noted above, may beeither manually drawn by users, or automatically determined, and theprocess at step 220 proceeds by identifying a position of at least oneof the vehicle detection zones and bicycle detection zones 105 innearest proximity to a stop bar, and calculating a height of apedestrian strip in the field of view 112 from the height of vehicledetection zone(s) 105 and the height of the bicycle detection zone(s)105. It should be noted that the process 200 does not require bothvehicle detection zones and bicycle zones 105, and therefore thepedestrian zone 104 may be calculated using one or both of these typesof zones 105. Additionally, one or more of each zone may be used todetermine and define the pedestrian zone 104 according to thisembodiment of the present invention.

Alternatively, the process 200 applies the analytical approach 162 todetermine and define pedestrian zones 104 at step 230, using movement ofone or more objects 107 in the field of view 112. As noted above, thisapproach 162 ascertains a region of interest 103 in the field of view112 for pedestrian tracks 108, based lane structures and intersectionroad markings 109 and movement of pixels representing moving objects107. Regardless of the approach used in either step 220 or step 230, theprocess 200 identifies a region of interest 103 in the form of apedestrian detection zone 104 for further processing of images to detectand count pedestrians 102.

At step 250, pixels in the region of interest 103 are processed toanalyze pixel content, using a combination of analytical approaches 170that examine characteristics of a person to separate groups of peopleand improve count accuracy. One such analytical approach 170 is apart-based object recognition approach 172 which identifies anindividual person from a group by using local features which are notaffected by occlusion as compared to global features. A single object,in this case a human pedestrian 102, can be thought of as having manyindividual parts like a head, arms, torso, legs, and each of those partscan be assigned a standard representative pixel size. Identification ofthese parts, and the relationship between them, can be used to recognizea person from a group, even if partly occluded.

In this approach, assumptions may be made to identify the parts in animage. For example, the head can be approximated as a circular shapedfeature, and the shoulders may be approximate as an arc in the image,such as using for example an edge feature space technique. Depending onthe camera location and the focal length, predetermined templates may beused to identify these parts using template matching techniques, such asfor example edge intensity template matching. Geometric constraintsrelative to the relationship between the parts may also be applied. Forexample, a constraint that the head cannot be next to the torso may beused to remove false matches. Additionally, other techniques such asboosted cascade like classifiers with edgelet features may be applied tolearn part detection. It is to be noted that parts can include fullbody, head, torso, shoulder, legs, head-shoulders and many othercombinations of such parts.

Another analytical approach 170 employed at step 250 is to develop amodel 173 of a single walking pedestrian using a histogram of orientedgradient features 172. Because pedestrians 102 often travel in groups,this may cause the ability to count pedestrians 102 accurately todegrade. The present invention therefore uses various characteristics ofpedestrians such as height, width, body shape, head shape, speed andlocation to separate each individual that may be in a group.

Over time, the process 200 creates a complex model 173 for the singlewalking pedestrian, based on all the ‘single walking pedestrians’ thathave been identified. The model 173 therefore continually evolves asmore data is collected within the present invention.

The single walking pedestrian model 173 is comprised of a histogram oforiented gradient features (HoG) 172 that include head-torso-leg bodystructure, body shape, body width, walking gestures, and others todefine a pedestrian contour. The process 200 computes this by analyzingportions of the field of view 112 in a sliding window that moves throughgrouped pedestrians in the image 111 to separate individual pedestriansfrom the grouped pedestrians based on the matching confidence betweenthe single walking pedestrian model 173 and the computed features of thecurrent content in the portions of the field of view 112 in the slidingwindow. The matching confidence is the mathematical interpretation ofthe similarity between the single walking pedestrian model 173 and thecomputed features of the current content of the sliding window. If thematching confidence is high, the process 200 concludes that a singlewalking pedestrian is found. If it is low, the analysis proceeds to thenext portion of the field of view 112 by moving the sliding window tothe next position and performs the comparison again, until it reachesthe end of the grouped pedestrians.

In a further embodiment, pedestrian detection using a HoG approach 172and a single walking pedestrian model 173 in step 250 therefore takes animage 111 from input data 110, and may create a multi-scale imagepyramid as the process 200 slides a moving window through the image tocompute HoG features. The process 200 may also apply one or morestatistical classifiers, such as for example SVM or the like, to detecta pedestrian using these HoG features. The process 200 learns by fusingresults of these statistical classifiers across all portions of thefield of view 112 in sliding window positions and different imagescales, and develops the model 173 to detect pedestrians 102.

Returning to FIG. 1, the pedestrian speed calibration component 145 isconfigured to calibrate a pedestrian speed with a region of interest 103in the field of view 112 for more accurate detection and counting ofpedestrians 102 in traffic intersection control. It is to be noted thatpedestrian speed calibration may be performed manually by a user orautomatically using one or more image processing steps as discussedbelow.

The pedestrian speed calibration component 145 performs automaticcalibration of pedestrian speed with a region of interest 103 in thefield of view 112 through a transformation of image pixels to actualdistance traveled of a pedestrian 102 in the image. Because of theconstant possibility of movement of sensors 120 such as cameras, andother changes such as focal length in the case of video cameras, thepedestrian speed calibration component 145 attempts a transformationfrom a pixel-based image 111 to an actual distance-based environment sothat a proper speed is calculated in relation to a defined pedestrianzone 104.

The pedestrian speed calibration component 145 uses the intersectionpavement markings and lane structures 109 to determine the speed atwhich a pedestrian 102 is moving in the field of view 112. Based on theposition of vehicle and bicycle detection zones 105 in the field of view112, the present invention detects the horizontal stop bar and lanelines to locate the stop bar location. A stop bar finding algorithm mayalso be applied to identify one or more horizontally straight whitelines in an image, by finding a peak in the horizontal projection. Thelayout of the traffic detection zone 114 may also be used to find thestop bar, as the bottom zones of each lane are typically close to thestop bar. Once the stop bar is found, the present invention attempts tofind lane lines which intersect with the stop bar. Zone coordinates arealso utilized to find most vertically-oriented lane lines, either to theleft or to the right of a vehicle detection zone 105.

The pedestrian speed calibration component 145 therefore performsautomatic calibration of pedestrian speed with a region of interest 103in the field of view 112 by initially identifying a location of one ormore of a stop bar and lane lines in the field of view 112, anddetermining an intersection of the lane lines with the stop bar todevelop coordinates of the region of interest 103. The pedestrian speedcalibration component 145 also identifies a vertical orientation of thelane lines relative to the stop bar.

The pedestrian speed calibration component 145 then computes features ofan image 111 inside the region of interest 103 to differentiate betweenimage pixels. Features analyzed may include edge gradients, thresholdedgrayscale pixels, and feature projections. The present invention thenmeasures an inter-lane distance between the image pixels using a knownlane line width and the vertical orientation of the lane lines relativeto the stop bar to map the image pixels to an actual distance traveledof a pedestrian 102 in the region of interest 103. Using thismeasurement and mapping, the pedestrian speed calibration component 145calculates a pedestrian speed from the actual distance traveled that iscalibrated with the region of interest 103. The calculation includescomputing the number of feet or meters traveled relative to lane linesand stop bar markings, and the distance per unit of time traveled by thepedestrian 102. The calibrated pedestrian speed may then be provided toa traffic controller system as output data 180, or other externaldevices or location for storage or use.

FIG. 3 is a flowchart illustrating steps in a process 300 for performingthe calibration of pedestrian speed in the pedestrian tracking andcounting framework 100, according to another embodiment of the presentinvention. Such a process 300 may include one or more algorithms forpedestrian speed calibration in a region of interest 103 within thecomponent 145.

The process 300 is initialized at step 310 by retrieving input data 110representing a field of view 112 in the traffic detection zone 114. Theprocess 300 analyzes this input data 110 to ascertain, at step 320, aregion of interest 103 in which pedestrians 102 may use the roadwaywithin the traffic detection zone 114. The region of interest 103 may ormay not be the specific pedestrian detection zone 104 referenced abovewith respect to other aspects of the present invention. Regardless, theprocess 300 determines the region of interest 103 using one or more ofpavement or intersection markings and lane structures 109, positions ofother detection zones 105, movement of objects 107 in the field of view112, or some combination of these approaches.

At step 330, the process 300 attempts to identify positions of both astop bar and lane lines for vehicles and bicycles in the region ofinterest 103. Using this information, the process 300 developspositional coordinates of the region of interest 103 at step 340. Thismay be performed in combination with the approach(es) used to ascertaina region of interest 103. Regardless, these zonal coordinates are usedto further identify vertical orientations of the lane lines relative tothe stop bar, so that those lane lines with the most verticalorientations relative to the detected stop bar are used for furthercomputations of pedestrian speed as noted below.

Once the region of interest 103 has been ascertained, the process 300then attempts to ascertain a relationship between an actual distancetraveled by a pedestrian 102 and image pixels in the input data 110 atstep 350. This involves measuring an inter-lane distance between theimage pixels at step 360, and mapping image pixels to the actualdistance traveled. This is performed using standard lane widths, so thatonce most vertical orientations of lane lines are established in step340, the transformation from an image to actual distance traveled by apedestrian 102 can be accomplished.

The process continues at step 370 by calculating pedestrian speed. Thisis performed as noted above by computing the distance, in number of feetor meters, traveled relative to lane lines and stop bar markings, andthe distance per unit of time traveled by the pedestrian 102. Thepedestrian speed is therefore calibrated to the region of interest 103for appropriate traffic intersection control, and the speed is providedas output data 180 to one or more of a traffic management tool 191,traffic management system 193, intersection signal controller 194,additional analytics 192, or any other additional or externalapplications 190.

Returning to FIG. 1, the incident detection component 146 is configuredto detect various pedestrian incidents and to provide an alarm as outputdata 180 when a pedestrian incident is determined. Incidents may includenon-moving objects within the pedestrian detection zone 104, or withinthe field of view 112 generally, that can cause abnormal pedestrian andvehicle movements. Incidents may also include prone objects orpedestrians 102 within the pedestrian detection zone 104, for examplepedestrians 102 have fallen to the pavement. Other types of incidentsinclude a presence of unauthorized vehicles in the pedestrian detectionzone 104.

Once the pedestrian detection zone 104 has been defined to track andidentify moving pedestrians 102 as discussed above, the incidentdetection component 146 learns the background of the pedestriandetection zone 104 to continually search for parts of that area that aredifferent than the background it has learned. If a change in thebackground has been present for some amount of time, and/or where movingvehicles (or even other walking pedestrians) being tracked are avoidingthe area that has changed to avoid contact, the incident detectioncomponent 146 concludes that non-moving objects are in the pedestriandetection zone 104 and generates a warning signal. Non-moving objectsmay include fallen pedestrians, stalled vehicles, objects that havefallen from moving vehicles, motorcyclists or bicyclists who are down,or objects placed in the pedestrian detection zone 104 by someone.

The incident detection component 146 tracks walking pedestrians 102 asthey move all the way through the pedestrian detection zone 104, fromthe entry point through to the exit point. If a pedestrian 102 stops atthe middle of the pedestrian detection zone 104 for some time, and doesnot move forward or backward and continues to be present in the zone104, then the present invention can issue an alarm signaling “pedestriandown in the roadway” to alert the responsible authorities.

The incident detection component 146 may also track movement ofvehicles, bicycles, motorcycles, and other objects 107 in the field ofview 112. Where it detects that an object 107 has entered the pedestriandetection zone 104, and stop there and not proceed for some time, theincident detection component 146 may signal that an unauthorized vehicleis present in the pedestrian detection zone 104 to alert authorities forfurther investigation.

FIG. 4 is a flow diagram illustrating steps in a process 400 of incidentdetection in the pedestrian tracking and counting framework 100according to one embodiment of the present invention. Such a process 400may include one or more algorithms for incident detection within thecomponent 146. In this process 400, the present invention receives animage 111 representing the field of view 112 in step 405 and therebyinitializes the incident detection component 146. At step 410, thepresent invention performs pedestrian detection using one or moremethods as described herein, and if a pedestrian 102 is identified atstep 420, proceeds with tracking the pedestrian 102 at step 430,together with updating the identification, location, speed, and othercharacteristics. If no pedestrian 102 is identified at step 420, thealgorithm loops back to begin processing a new image 111 representingthe field of view 112.

The algorithm for incident detection in the component 146 thendetermines if the pedestrian 102 is moving at step 440. If thepedestrian is found to be in motion, the process 400 returns to beginprocessing a new image 111 representing the field of view 112. If,however, the pedestrian 102 is not in motion at step 440, the algorithmfor incident detection proceeds to determine how long the pedestrian 102has been stationary at step 450. If the pedestrian 102 is not in motionin excess of a certain amount of time, a pedestrian down alarm isgenerated at step 470 as output data 180.

The certain amount of time may be preset by a user, and may also belearned by the process 400 as pedestrians 102 and other objects 107 areidentified and tracked. A timer may be updated at step 460 fordetermining whether a pedestrian 102 is not in motion for a certainamount of time, and this value is returned to the beginning of thealgorithm. In this matter, were the incident detection components thatpedestrians 102 are not in motion for some specific reason (for example,a blockage in traffic) then this value can be stored and used by theprocess 400.

In one embodiment of the present invention, the pedestrian tracking andcounting framework 100 may be configured to provide a separate output180 to a traffic signal controller 194 when a group of pre-determinedpeople is identified to enable additional functions to be performed. Auser may set sample size for this output 180 using the trafficmanagement tool 191, or it may be automatically determined within thepresent invention.

Regardless, several applications are possible with an identified groupas an output 180. For example, the traffic signal controller 194 mayextend the walk time or hold a red light for vehicles to allow safepassage through the intersection. In another example, the presentinvention may use an identified group of people to further identifyperiods of high pedestrian traffic for better intersection efficiency.It is therefore to be understood that many uses of output data 180 inapplications for traffic intersection signal control are possible andwithin the scope of the present invention.

The pedestrian tracking and counting framework 100 of the presentinvention may be applied in many different circumstances. For example,the present invention may be used to identify pedestrians 102 duringadverse weather conditions when physical danger may increase due toreduced visibility. The present invention may therefore performpedestrian detection in low-light, fog or other low-contrast conditionsfor improved roadway and intersection.

In another example, the present invention may be used to identify thedifference between a pedestrian 102 and the pedestrian's shadow. In suchan example, the pedestrian detection is improved through rejection ofpedestrian shadows to ensure improved accuracy in pedestrian detectionand counting.

In still a further example, the present invention may be used todetermine a normal or average crossing speed for pedestrians 102 in adetection zone 104. This may be then be used to identify slow-movingpedestrians 102, such as the elderly, children, and disabled orwheelchair-bound persons, to extend and/or adjust a signal timing forcrossing the intersection for safer passage. It may also be used toidentify faster-moving intersection users, such as pedestrians 102 usinghover boards, skateboards, or other such devices in the pedestriandetection zone 104.

The present invention may further be used to identify late arrivals inthe pedestrian detection zone 104, to extend and/or adjust signal timingfor safe intersection passage. The present invention may also receiveand use additional input from the traffic signal controller to identifywhen a pedestrian 102 starts to cross the intersection after a certainpercentage of the crossing time has expired. The present invention mayalso be utilized to compute a crosswalk occupancy, for example todetermine a pedestrian density in the detection zone 104.

As noted above, the pedestrian tracking and counting framework 100, andthe various processes described herein, may be utilized in combinationwith existing approaches to determining vehicle and bicycle detectionzones 105, and may be therefore performed using the existing field ofview 112 in a traffic detection zone 114 that is designed to detectvehicles, bicycles and other road users needing the traffic signal tocross an intersection.

In one embodiment, in order to achieve better accuracy, the presentinvention may use an existing vehicle detection status, such as speed orsaturation, to dynamically change the sensitivity of pedestriandetection. For example, a known vehicular status may be applied toincrease the likelihood of pedestrian crossing when stopped vehicle isdetected, or when no vehicle in present. Conversely, it may be used todecrease the likelihood of pedestrian crossing while vehicular trafficis freely flowing. Therefore, the present invention use knowledge ofeither stopped or moving vehicles or bicycles in the respective otherdetection zones 105 or moving vehicles to improve pedestrian detectionaccuracy.

Similarly, the present invention may be part of a whole scene analysisthat combines vehicular, bicycle, and pedestrian detection to identifydifferent moving objects 107, such as vehicles, motorcycles, bicyclesand pedestrians. Each object type has its own unique characteristics,and the present invention is configured to automatically learn theseunique characteristics and apply them to identify the different types.Output data 180 from such a whole scene analysis provides trafficengineers, responsible authorities, and the public with a completepicture of street and intersection activity (for example, who is usingwhat and at what time and for how long) for improved roadway management.

As noted above, the pedestrian tracking and counting framework 100 maybe configured to learn features of a traffic intersection, such as thebackground, using the image processing paradigms discussed herein. Thismay further include one or more approaches for learning roadway lanestructures for improving accuracy of in identifying vehicles, bicycles,pedestrians, and other objects 107 in a traffic detection zone 114.

Consider a scenario where a lane line is detected to the left side of afield of view 112, and its feature signature was learned over time toaccount for how much natural variability can be expected. Considerfurther that this lane line structure gets occluded 80% of the time avehicle is detected inside the traffic detection zone 114. It can beinferred that when the lane line gets occluded then it is possible thata vehicle is likely present inside the traffic detection zone 114,thereby increasing a detection rate. Also, consider the case where acurb was detected to the right side of a zone and it was learned thatthe curb gets occluded 90% of the time when a vehicle is present insidethe traffic detection zone 114. This can be used to reduce a nuisance orfalse call rate, which can be caused by shadows or portions of vehiclespresent in the neighboring zone that can confuse the existing imageanalysis algorithms to misconstrue the contents of a zone. Wheredetection and false call rates are key metrics used to measure accuracy,having low missed and false calls improves the overall performance andefficiency of a traffic management system.

The present invention may include an approach that incorporates a highlyrobust model that learns roadway structures to improve sensing accuracyof a traffic management system. Such a roadway structure model providesa high confidence that learned structures correspond to physical roadwaystructures, and is robust to short-term changes lighting conditions,such as shadows cast by trees, buildings, clouds, vehicles, rain, fog,etc. The model also adaptively learns long-term appearance changes ofthe roadway structures caused by natural wear and tear, seasonal changesin lighting (winter vs. summer), etc. The model also exhibits a lowdecision time (in milliseconds) for responding to occlusions caused byfast moving traffic, and low computational complexity capable of runningon an embedded computing platform.

In one exemplary embodiment, the present invention looks at user-drawnzones 105 to initialize and establish borders for regions of interest103 for various detection zones. Images 111 are processed to computefeatures inside borders for the region of interest 103, and find roadwaystructures using these computed features. The model is then developed tolearn background structures from these features to detect an occlusion,and learn the relationship between structure occlusions and detectionzones 105.

Several roadway characteristics may aid in the model's ability to learnthe background and relationship between structure occlusions anddetection ones. For example, roadway structures such as lane lines,curbs, and medians are generally found adjoining detection zoneboundaries. Also, roadway structures exhibit strong feature patternsthat can be generalized. For example, they contain strong edges and arerelatively bright in grayscale. Such structures can be effectivelydescribed by overlapping projector peaks of positive edges, negativeedges and thresholded grayscale pixels. These structures are alsopersistent, and their feature signatures can be learned over time todetect occlusions and draw inferences regarding the presence of vehiclesin the neighboring zones.

In the modeling approach described above, every zone requires thecomputation of a left and a right border region of interest 103. If twozones are considered horizontal neighbors, then they will share a borderregion of interest 103, and the area between the zones is established asthe border region of interest 103. If a zone has no neighboring zones tothe left or right, then the corresponding the boundary of thecorresponding side is extended by an area proportionate to the zonewidth, and this extended area serves as the border region of interest103 for the zone. Also, each border region of interest 103 may besub-divided into tile regions of interest based on the size of theuser-drawn zones. A larger zone provides a larger border area, allowingthe model to work with smaller tiles that provide a more localizedknowledge of structures and occlusion.

Features are computed in the border region of interest 103 by computingedges from projecting positive and negative edges across rows, andfinding peak segments from each projected positive and negative edge.Additionally, the peak segments may be determined by computing a grayhistogram and a cumulative histogram from image pixels, determining agray threshold image, and projecting resulting pixels across rows.Roadway structures are learned from each computed feature by findingoverlapping feature segment locations, accumulating peak segmentlocations of overlapping features in a histogram, and finding peaks inthe feature background histograms. The model of roadway structures istherefore established using feature histogram peak locations. This isused to identify an occlusion by finding overlapping positive edge peaksegments, negative edge peak segments, and gray threshold peak segmentswith the background histogram. Matching scores are compute for each ofthese overlaps and compared to threshold values to differentiate betweena visible structure and an occlusion.

The systems and methods of the present invention may be implemented inmany different computing environments. For example, they may beimplemented in conjunction with a special purpose computer, a programmedmicroprocessor or microcontroller and peripheral integrated circuitelement(s), an ASIC or other integrated circuit, a digital signalprocessor, electronic or logic circuitry such as discrete elementcircuit, a programmable logic device or gate array such as a PLD, PLA,FPGA, PAL, and any comparable means. In general, any means ofimplementing the methodology illustrated herein can be used to implementthe various aspects of the present invention. Exemplary hardware thatcan be used for the present invention includes computers, handhelddevices, telephones (e.g., cellular, Internet enabled, digital, analog,hybrids, and others), and other such hardware. Some of these devicesinclude processors (e.g., a single or multiple microprocessors orgeneral processing units), memory, nonvolatile storage, input devices,and output devices. Furthermore, alternative software implementationsincluding, but not limited to, distributed processing, parallelprocessing, or virtual machine processing can also be configured toperform the methods described herein.

The systems and methods of the present invention may also be wholly orpartially implemented in software that can be stored on a non-transitorycomputer-readable storage medium, executed on programmed general-purposecomputer with the cooperation of a controller and memory, a specialpurpose computer, a microprocessor, or the like. In these instances, thesystems and methods of this invention can be implemented as a programembedded on a mobile device or personal computer through such mediums asan applet, JAVA® or CGI script, as a resource residing on one or moreservers or computer workstations, as a routine embedded in a dedicatedmeasurement system, system component, or the like. The system can alsobe implemented by physically incorporating the system and/or method intoa software and/or hardware system.

Additionally, the data processing functions disclosed herein may beperformed by one or more program instructions stored in or executed bysuch memory, and further may be performed by one or more modulesconfigured to carry out those program instructions. Modules are intendedto refer to any known or later developed hardware, software, firmware,artificial intelligence, fuzzy logic, expert system or combination ofhardware and software that is capable of performing the data processingfunctionality described herein.

The foregoing descriptions of embodiments of the present invention havebeen presented for the purposes of illustration and description. It isnot intended to be exhaustive or to limit the invention to the preciseforms disclosed. Accordingly, many alterations, modifications andvariations are possible in light of the above teachings, may be made bythose having ordinary skill in the art without departing from the spiritand scope of the invention. It is therefore intended that the scope ofthe invention be limited not by this detailed description. For example,notwithstanding the fact that the elements of a claim are set forthbelow in a certain combination, it must be expressly understood that theinvention includes other combinations of fewer, more or differentelements, which are disclosed in above even when not initially claimedin such combinations.

The words used in this specification to describe the invention and itsvarious embodiments are to be understood not only in the sense of theircommonly defined meanings, but to include by special definition in thisspecification structure, material or acts beyond the scope of thecommonly defined meanings. Thus if an element can be understood in thecontext of this specification as including more than one meaning, thenits use in a claim must be understood as being generic to all possiblemeanings supported by the specification and by the word itself.

The definitions of the words or elements of the following claims are,therefore, defined in this specification to include not only thecombination of elements which are literally set forth, but allequivalent structure, material or acts for performing substantially thesame function in substantially the same way to obtain substantially thesame result. In this sense it is therefore contemplated that anequivalent substitution of two or more elements may be made for any oneof the elements in the claims below or that a single element may besubstituted for two or more elements in a claim. Although elements maybe described above as acting in certain combinations and even initiallyclaimed as such, it is to be expressly understood that one or moreelements from a claimed combination can in some cases be excised fromthe combination and that the claimed combination may be directed to asub-combination or variation of a sub-combination.

Insubstantial changes from the claimed subject matter as viewed by aperson with ordinary skill in the art, now known or later devised, areexpressly contemplated as being equivalently within the scope of theclaims. Therefore, obvious substitutions now or later known to one withordinary skill in the art are defined to be within the scope of thedefined elements.

The claims are thus to be understood to include what is specificallyillustrated and described above, what is conceptually equivalent, whatcan be obviously substituted and also what essentially incorporates theessential idea of the invention.

The invention claimed is:
 1. A method, comprising: receiving input datarepresenting a field of view of a traffic detection zone; analyzing theinput data within a computing environment in one or more data processingmodules executed in conjunction with at least onespecifically-configured processor, the one or more data processingmodules configured to a) identify a region in the field of view of thetraffic detection zone used by one or more pedestrians, and b)accurately count the one or more pedestrians in the traffic detectionzone, by 1) defining a pedestrian zone in a field of view of a trafficdetection zone, by a) ascertaining a region of interest in the field ofview for pedestrian tracks based on at least one of lane structures andintersection road markings, and on movement of pixels representingmoving objects relative to the at least one of lane structures andintersection road markings, b) determining accumulated tracks of themoving objects in the field of view, by analyzing motion strength andfrequency of activity of each pixel representing the moving objects infield of view, and c) tracking pedestrian characteristics that includeone or more of size, gestures, speed, entry points, and exit points inthe region of interest to distinguish the accumulated tracks of themoving objects from the pedestrian tracks; 2) counting the one or morepedestrians in the pedestrian zone by, a) analyzing portions of thepedestrian zone in the field of view, b) computing features of currentpixel content identified in the analyzed portions by identifyingpart-based features defining an individual pedestrian that include oneor more of body structure combinations, body shape, body width orwalking gestures, c) developing a model of a single walking pedestrianto separate each individual pedestrian in a group of moving pedestriansin the field of view, by computing pedestrian features using pixelsdefining a pedestrian contour, d) determining a matching confidencebetween an individual pedestrian and a group of moving pedestrians bycalculating a mathematical similarity between the computed features ofcurrent pixel content and the model of the single walking pedestrian,and e) incrementing a pedestrian count where a high matching confidenceindicates that an individual pedestrian has been identified; andgenerating, as output data, the pedestrian count.
 2. The method of claim1, wherein the determining a matching confidence between an individualpedestrian and a group of moving pedestrians further comprisesidentifying an individual pedestrian where a matching confidence ishigh, and analyzing a next portion of the field of view where a matchingconfidence is low.
 3. The method of claim 1, wherein the identifyingpart-based features defining an individual pedestrian further comprisesone or more of determining a width and a height of one or more objectparts, comparing body structure combinations with one or morepredetermined templates, and applying one or more geometric constraintsto separate the part-based features.
 4. The method of claim 1, furthercomprising creating and continually refining a boundary of thepedestrian zone based on the accumulated tracks of the moving objects inthe region of interest.
 5. The method of claim 1, further comprisingadjusting sensitivity for detecting the one or more pedestrians in thepedestrian zone by applying a vehicle detection status, the vehicledetection status including one or more of vehicular speed and trafficdetection zone saturation, and dynamically changing the sensitivity fordetecting the one or more pedestrians in the pedestrian zone by one orboth of increasing a likelihood of a pedestrian crossing when a stoppedvehicle is detected or no vehicle in present, and decreasing alikelihood of a pedestrian crossing while the vehicle traffic is freeflowing.
 6. The method of claim 1, wherein the input data is captured byone or more sensors in or near a traffic intersection, the one or moresensors including at least one of video cameras, a radar system, and amagnetometer.
 7. The method of claim 1, further comprising providing theoutput data to a traffic management tool configured to enable a user toselect a size and location of one or more of the field of view and theat least one of lane structures and intersection road markings, the lanestructures and intersection road markings defining one or more vehicledetection zones and a stop bar.
 8. The method of claim 1, wherein thedefining a pedestrian zone in a field of view of a traffic detectionzone further comprises detecting pixel motion by identifying an initialmotion threshold for pixel activity in the field of view, and updatingthe initial motion threshold for pixel activity in the field of view aspixels move through the field of view.
 9. The method of claim 1, whereinthe defining a pedestrian zone in a field of view of a traffic detectionzone further comprises computing a binary thresholded image defining ahistogram of oriented gradient features from the pixel activity, andupdating the histogram of orient gradient features of the computed imageas activity progresses in the field of view.
 10. The method of claim 1,wherein the defining a pedestrian zone in a field of view of a trafficdetection zone further comprises computing a frequency of the pixelactivity and determining tracks of pixel motion based on the frequencyof pixel activity.
 11. The method of claim 1, wherein the determiningaccumulated tracks of the moving objects in the field of view furthercomprises determining vehicle and bicycle tracks in the trafficdetection zone from the at least one of lane structures and intersectionroad markings.
 12. A method of pedestrian detection and counting fortraffic intersection control, comprising: distinguishing one or morepedestrians from moving pixels representing other objects in a field ofview of a traffic detection zone to define a pedestrian zone, by a)ascertaining a region of interest for pedestrian tracks within the fieldof view based on at least one of lane structures and intersection roadmarkings, and on movement of pixels representing moving objects relativeto the at least one of lane structures and intersection road markings,b) detecting movement of the moving objects within the traffic detectionzone from pixel activity by computing a binary thresholded imagedefining a histogram of oriented gradient features to analyze motionstrength, and computing a frequency of activity of each pixelrepresenting the moving objects in field of view to identify accumulatedtracks of the moving objects, and c) differentiating the accumulatedtracks of the moving objects from the pedestrian tracks by trackingpedestrian characteristics that include one or more of size, gestures,speed, entry points, and exit points in the region of interest; anddetecting the one or more pedestrians in the pedestrian area fromsimilarities of a model of a single walking pedestrian with part-basedobject recognition of individual pedestrians, by a) computing featuresof current pixel content identified by analyzing portions of thepedestrian zone in the field of view to define individual pedestriansfrom the part-based object recognition of one or more of body structurecombinations, body shape, body width or walking gestures, c) computingpedestrian features using pixels defining a pedestrian contour todevelop the model of the single walking pedestrian to separate eachindividual pedestrian from a group of moving pedestrians in the field ofview, d) determining a matching confidence between an individualpedestrian and a group of moving pedestrians by calculating amathematical similarity between the computed features of current pixelcontent and the model of the single walking pedestrian, and identifyingan individual pedestrian where a matching confidence is high, andanalyzing a next portion of the pedestrian zone in the field of viewwhere a matching confidence is low.
 13. The method of claim 12, furthercomprising incrementing a pedestrian count where a high matchingconfidence indicates that an individual pedestrian has been identified.14. The method of claim 13, further comprising generating an outputrepresenting the pedestrian count to an external device or location forstorage or use.
 15. The method of claim 12, further comprising providingthe pedestrian count to a traffic management tool, wherein the trafficmanagement tool is configured to enable a user to select a size andlocation of one or more of the field of view and the at least one oflane structures and intersection road markings, the lane structures andintersection road markings defining one or more vehicle detection zonesand a stop bar.
 16. The method of claim 12, wherein the identifyingpart-based features defining an individual pedestrian further comprisesone or more of determining a width and a height of one or more objectparts, comparing body structure combinations with one or morepredetermined templates, and applying one or more geometric constraintsto separate the part-based features.
 17. The method of claim 12, furthercomprising creating and continually refining a boundary of thepedestrian zone based on the accumulated tracks of the moving objects inthe region of interest.
 18. The method of claim 12, further comprisingadjusting sensitivity for detecting the one or more pedestrians in thepedestrian zone by applying a vehicle detection status, the vehicledetection status including one or more of vehicular speed and trafficdetection zone saturation, and dynamically changing the sensitivity fordetecting the one or more pedestrians in the pedestrian zone by one orboth of increasing a likelihood of a pedestrian crossing when a stoppedvehicle is detected or no vehicle in present, and decreasing alikelihood of a pedestrian crossing while the vehicle traffic is freeflowing.
 19. The method of claim 12, wherein the field of view iscaptured by one or more sensors in or near a traffic intersection, theone or more sensors including at least one of video cameras, a radarsystem, and a magnetometer.
 20. The method of claim 12, wherein thedifferentiating the accumulated tracks of the moving objects from thepedestrian tracks further comprises determining vehicle and bicycletracks in the traffic detection zone from the at least one of lanestructures and intersection road markings.
 21. A system, comprising: acomputing environment including at least one non-transitorycomputer-readable storage medium having program instructions storedtherein and a computer processor operable to execute the programinstructions within one or more data processing modules configured to a)identify a region in a field of view of a traffic detection zone used byone or more pedestrians, and b) accurately count the one or morepedestrians in the traffic detection zone, the one or more dataprocessing modules including: a pedestrian count zone identificationmodule configured to define a pedestrian zone in the field of view, bya) ascertaining a region of interest in the field of view for pedestriantracks based on at least one of lane structures and intersection roadmarkings, and on movement of pixels representing moving objects relativeto the at least one of lane structures and intersection road markings,b) determining accumulated tracks of the moving objects in the field ofview, by analyzing motion strength and frequency of activity of eachpixel representing the moving objects in field of view, and c) trackingpedestrian characteristics that include one or more of size, gestures,speed, entry points, and exit points in the region of interest todistinguish the accumulated tracks of the moving objects from thepedestrian tracks; a pedestrian detector learning module configured tocount the one or more pedestrians in the pedestrian zone by, a)analyzing portions of the pedestrian zone in the field of view, b)computing features of current pixel content identified in the analyzedportions by identifying part-based features defining an individualpedestrian that include one or more of body structure combinations, bodyshape, body width or walking gestures, c) developing a model of a singlewalking pedestrian to separate each individual pedestrian in a group ofmoving pedestrians in the field of view, by computing pedestrianfeatures using pixels defining a pedestrian contour, d) determining amatching confidence between an individual pedestrian and a group ofmoving pedestrians by calculating a mathematical similarity between thecomputed features of current pixel content and the model of the singlewalking pedestrian, and e) incrementing a pedestrian count where a highmatching confidence indicates that an individual pedestrian has beenidentified; and an output module configured to communicate thepedestrian count to an external device or location.
 22. The system ofclaim 21, wherein the pedestrian detector learning module is furtherconfigured to identify an individual pedestrian where a matchingconfidence is high, and analyzing a next portion of the pedestrian zonein the field of view where a matching confidence is low.
 23. The systemof claim 21, wherein the pedestrian detector learning module is furtherconfigured to count the one or more pedestrians in the pedestrian zoneby one or more of determining a width and a height of one or more objectparts, comparing body structure combinations with one or morepredetermined templates, and applying one or more geometric constraintsto separate the part-based features.
 24. The system of claim 21, whereinthe pedestrian detector learning module is further configured to adjustsensitivity for detecting the one or more pedestrians in the pedestrianzone by applying a vehicle detection status that includes one or more ofvehicular speed and traffic detection zone saturation, and dynamicallychange the sensitivity for detecting the one or more pedestrians in thepedestrian zone by one or both of increasing a likelihood of apedestrian crossing when a stopped vehicle is detected or no vehicle inpresent, and decreasing a likelihood of a pedestrian crossing while thevehicle traffic is free flowing.
 25. The system of claim 21, wherein thefield of view is captured by one or more sensors in or near a trafficintersection, the one or more sensors including at least one of videocameras, a radar system, and a magnetometer.
 26. The system of claim 21,wherein the pedestrian count is provided to a traffic management toolconfigured to enable a user to select a size and location of one or moreof the field of view and the at least one of lane structures andintersection road markings, the lane structures and intersection roadmarkings defining one or more vehicle detection zones and a stop bar.27. The system of claim 21, wherein the pedestrian count zoneidentification module is further configured to detect pixel motion byidentifying an initial motion threshold for pixel activity in the fieldof view, update the initial motion threshold for pixel activity in thefield of view as pixels move through the field of view, compute a binarythresholded image defining a histogram of oriented gradient featuresfrom the pixel activity, and update the histogram of orient gradientfeatures of the computed image as activity progresses in the field ofview.
 28. The system of claim 21, wherein the pedestrian count zoneidentification module is further configured to compute a frequency ofthe pixel activity and determine tracks of pixel motion based on thefrequency of pixel activity.
 29. The system of claim 21, wherein thepedestrian count zone identification module is further configured todetermine vehicle and bicycle tracks in the traffic detection zone fromthe at least one of lane structures and intersection road markings. 30.The system of claim 21, wherein the pedestrian count zone identificationmodule is further configured to create and continually refine a boundaryof the pedestrian zone based on the accumulated tracks of the movingobjects in the region of interest.